Mutation Analysis of Various Cancer Types

Introduction/Background

  • Study Cohort: 1661 patients/samples.
  • Objective: Identify predictive biomarkers for Immune Checkpoint Inhibitors (ICI).
  • Hypothesis: Does Tumor Mutational Burden (TMB) correlate with survival?
    • Concept: Higher TMB \(\rightarrow\) Higher response?

From Samstein et al., Nat Genet 2019

Materials

  • Sequencing: MSK-IMPACT (Next Generation Sequencing).

  • Panels Used:

    • IMPACT 3 (341 genes)
    • IMPACT 5 (410 genes)
    • IMPACT 6 (468 genes)

  • Note: Higher mutation counts detected as more genes are analyzed.

  • Datasets:

    • data_clinical_patient.txt
    • data_clinical_sample.txt
    • data_gene_panel_matrix.txt
    • data_mutations.txt
    • data_sv.txt

Methods

Survival Analysis

  • Package: survival
  • Function: Surv()
    • Parameters: Survival time + Status (0=Living, 1=Deceased).
  • Estimator: survfit() (Kaplan-Meier).
  • Data: clinical_patients.
  • Output: Step curve stratified by parameters (sex, age, cancer type).

Cox Regression

  • Package: survival
  • Goal: Hazard risk study.
  • Formula: \(\lambda(t,j) = \lambda_0(t) \cdot \exp(\sum \beta_i X_i)\)
    • \(\lambda\): Survival probability for individual \(j\) at time \(t\).
    • \(X_i\): Explanatory variables.
  • Hazard Ratio: \(\displaystyle \frac{\lambda(t,j_1)}{\lambda(t,j_2)} = \exp(\beta_i)\)

GSEA

  • Gene Set Enrichment Analysis
  • Source: GO Data from Molecular Signature Database.
  • Metric: Ranked list based on numbers of mutated Samples per Gene.
  • Function: fgseaMultilevel() from fgsea package.

Results: Overview - Survival Analysis & Mutation Types

Results: Structural Variants (SV)

Structural Variants (SVs) are large-scale genomic rearrangements that can significantly disrupt gene function.

EGFR is the most significantly overrepresented gene when aggregating both the beginning and ending sites of the SVs.

Results: EGFR Focus in Glioma

EGFR is a receptor tyrosine kinase frequently altered in cancer. Its activation drives uncontrolled cell growth and proliferation. *

* From Yarden & Sliwkowski, Nat Rev Mol Cell Biol 2001

Results: Mutations in Glioma

  • EGFR is not a primary mutational target

  • Alternative Pathways: Genes like TERT and TP53 show a much higher incidence of pinpoint mutations.

    → distinct biological pathways compared to the SV-driven EGFR pathway.

Results: GSEA (SV & Mutations)

Significant Pathways in Glioma

Biological Process pvalue adjusted pvalue
GOBP_CIRCULATORY_SYSTEM_DEVELOPMENT 1.87e-06 0.00018
GOBP_NEGATIVE_REGULATION_OF_DEVELOPMENTAL_PROCESS 3.91e-07 0.00018
GOBP_REGULATION_OF_APOPTOTIC_SIGNALING_PATHWAY 8.07e-07 0.00018
GOBP_REGULATION_OF_CELLULAR_LOCALIZATION 1.65e-06 0.00018
GOBP_REGULATION_OF_MITOTIC_CELL_CYCLE_PHASE_TRANSITION 1.38e-06 0.00018
GOBP_TUBE_DEVELOPMENT 3.69e-06 0.00030
GOBP_CELLULAR_RESPONSE_TO_OXYGEN_LEVELS 7.12e-06 0.00039
GOBP_CELL_CYCLE_G1_S_PHASE_TRANSITION 6.76e-06 0.00039
GOBP_REGULATION_OF_CELL_CYCLE_G1_S_PHASE_TRANSITION 6.40e-06 0.00039
GOBP_MITOTIC_CELL_CYCLE_PHASE_TRANSITION 8.29e-06 0.00040
GOBP_BLOOD_VESSEL_MORPHOGENESIS 1.71e-05 0.00070
GOBP_REGULATION_OF_EXTRINSIC_APOPTOTIC_SIGNALING_PATHWAY 1.58e-05 0.00070
GOBP_APOPTOTIC_SIGNALING_PATHWAY 2.78e-05 0.00105
GOBP_CELL_AGING 3.01e-05 0.00106
GOBP_EXTRINSIC_APOPTOTIC_SIGNALING_PATHWAY 3.40e-05 0.00112

Results: Confounders in Glioma

Discussion

  • Tumor Burden Hypothesis:
    • General trend: Higher TMB \(\rightarrow\) higher survival.
  • Severity Indication:
    • Surprisingly, more mutations do not necessarily indicate a higher severity of cancer in terms of immediate survival.
  • The Glioma Exception:
    • Glioma appears to be an exemption to the TMB rule.
    • No clear correlation observed in our subset analysis.